High Energy Physics experiments often rely on Monte-Carlo event generators. Such generators often contain a large number of parameters and need fine-tuning to closely match experimentally observed data. This task traditionally requires expert knowledge of the generator and the experimental setup as well as vast computing power.Generative Adversarial Networks (GAN) is a powerful method to match distribution of samples produced by a parametrized generator to a set of observations. Following the recently proposed study on adversarial variational optimization of non-differentiable generator, we adopt Bayesian Optimization as an efficient gradient-free optimization method for adversarial fine-tining of event generators. The proposed method requires minimal prior knowledge,nevertheless, allows for expert insights to be straightforwardly incorporated into the method.In this talk, we briefly describe a theoretical approach to the problem and show the results for parameter tunning of PYTHIA event generator.